Prescriptive analytics for FIFA World Cup lodging capacity planning Ahmed Ghoniem 1 * , Agha Iqbal Ali 1 , Mohammed Al-Salem 2 and Wael Khallouli 2 1 Department of Operations and Information Management, Isenberg School of Management, University of Massachusetts, Amherst, MA 01003, USA; and 2 Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar The FIFA World Cup, comprising sixty-four matches spanning an entire month, has, in recent years, been attended by about three million spectators of which over half a million are visitors requiring lodging. Planning lodging capacity for an event of this magnitude is necessary for host nations where pre-existing infrastructures are either inadequate or lacking. This paper develops an optimization analytics framework that sequentially employs two integer programming models for foreign spectator analysis and the consequent lodging requirements. The framework is applied to assess the preparedness of lodging infrastructure in Qatar for FIFA 2022. Journal of the Operational Research Society (2017) 68(10), 1183–1194. doi:10.1057/s41274-016-0143-x; published online 15 December 2016 Keywords: capacity planning; FIFA World Cup; optimization; prescriptive analytics 1. Introduction Examination of existing and planned infrastructures for holding football matches and lodging foreign spectators is central to hosting the FIFA World Cup. Infrastructure planning for Qatar 2022, with much of the lodging capacity yet to be constructed, is unique and departs from past FIFA World Cups. Lodging infrastructure has not been an issue for recent FIFA World Cups since matches have been held in cities with large populations and an established hotel industry. The currency of examination of the potential demand for lodging is a consequence of the expectation that much of the attendance will be made up of foreign spectators given that the entire country’s population is a mere fraction of that of most host cities in past World Cups. The total population in the host cities was over twenty million for South Africa 2010, over thirty-five million for Brazil 2014 (with individual host city populations ranging from about half a million to over eleven million), and over twenty-four million for Russia 2018. Qatar, which has the third highest GDP per capita, has committed $20 billion for investment in tourism infrastructure under the ‘‘Qatar 2030 Vision’’ for economic growth and development (Cighi and Gandhi, 2011). Planning expansion of Qatar’s hotel capacity of 13,123 rooms in 83 approved hotels, in 2013, is necessary years in advance of the upcoming World Cup to allow for construction lead time. To determine appropriate capacity for lodging, it is necessary to account for, and incorporate, likely variation in foreign spectator attendance. The variation that must be taken into account is contributed to by the makeup of the qualifying teams, the buying power in, and the ability of fans to travel from, the associated nations, the groups for the World Cup that are constituted by FIFA, and the detailed match schedule. As such this work lies at the interface of attendance forecasting and scheduling. The work furthers the existing body of literature on operations research studies in sports management that has, hitherto, not included lodging capacity analytics. Foreign spectator attendance is influenced by the buying power in nations, and hence, multiple scenarios that differ in the composition of qualifying teams must be considered. For each scenario, the total number of spectators will depend on the capacity of the stadiums in which matches are held. Hence the specific match schedule must be taken into consideration. In Section 1.1, we familiarize the reader with the FIFA process of group formation and the World Cup schedule characteristics. In Section 1.2, we review the relevant literature on sports and tourism management. In Section 1.3, we provide an outline of the paper. 1.1. Background Since 1998, teams from thirty-two nations drawn from six football confederations 1 compete in the FIFA World Cup. The 1 Asian Football Confederation (AFC); Confe ´de ´ration Africaine de Football (CAF); The Confederation of North, Central America and Caribbean Association Football (CONCACAF); Confederacio ´n Sudamer- icana de Fu ´tbol (CONMEBOL); Oceania Football Confederation (OFC); Union des Associations Europe ´ennes de Football (UEFA). *Correspondence: Ahmed Ghoniem, Department of Operations and Information Management, Isenberg School of Management, University of Massachusetts, Amherst, MA 01003, USA. E-mail: [email protected]Journal of the Operational Research Society (2017) 68, 1183–1194 ª 2016 The Operational Research Society. All rights reserved. 0160-5682/17 www.palgrave.com/journals
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Prescriptive analytics for FIFA World Cup lodgingcapacity planningAhmed Ghoniem1*, Agha Iqbal Ali1, Mohammed Al-Salem2 and Wael Khallouli2
1Department of Operations and Information Management, Isenberg School of Management, University of
Massachusetts, Amherst, MA 01003, USA; and 2Department of Mechanical and Industrial Engineering, Qatar
University, Doha, Qatar
The FIFA World Cup, comprising sixty-four matches spanning an entire month, has, in recent years, beenattended by about three million spectators of which over half a million are visitors requiring lodging. Planninglodging capacity for an event of this magnitude is necessary for host nations where pre-existing infrastructures areeither inadequate or lacking. This paper develops an optimization analytics framework that sequentially employstwo integer programming models for foreign spectator analysis and the consequent lodging requirements. Theframework is applied to assess the preparedness of lodging infrastructure in Qatar for FIFA 2022.
Journal of the Operational Research Society (2017) 68(10), 1183–1194. doi:10.1057/s41274-016-0143-x;
published online 15 December 2016
Keywords: capacity planning; FIFA World Cup; optimization; prescriptive analytics
1. Introduction
Examination of existing and planned infrastructures for
holding football matches and lodging foreign spectators is
central to hosting the FIFA World Cup. Infrastructure planning
for Qatar 2022, with much of the lodging capacity yet to be
constructed, is unique and departs from past FIFA World
Cups. Lodging infrastructure has not been an issue for recent
FIFA World Cups since matches have been held in cities with
large populations and an established hotel industry. The
currency of examination of the potential demand for lodging is
a consequence of the expectation that much of the attendance
will be made up of foreign spectators given that the entire
country’s population is a mere fraction of that of most host
cities in past World Cups. The total population in the host
cities was over twenty million for South Africa 2010, over
thirty-five million for Brazil 2014 (with individual host city
populations ranging from about half a million to over eleven
million), and over twenty-four million for Russia 2018. Qatar,
which has the third highest GDP per capita, has committed $20
billion for investment in tourism infrastructure under the
‘‘Qatar 2030 Vision’’ for economic growth and development
(Cighi and Gandhi, 2011).
Planning expansion of Qatar’s hotel capacity of 13,123 rooms
in 83 approved hotels, in 2013, is necessary years in advance of
the upcomingWorld Cup to allow for construction lead time. To
determine appropriate capacity for lodging, it is necessary to
account for, and incorporate, likely variation in foreign spectator
attendance. The variation that must be taken into account is
contributed to by the makeup of the qualifying teams, the buying
power in, and the ability of fans to travel from, the associated
nations, the groups for the World Cup that are constituted by
FIFA, and the detailed match schedule. As such this work lies at
the interface of attendance forecasting and scheduling. The work
furthers the existing body of literature on operations research
studies in sports management that has, hitherto, not included
lodging capacity analytics. Foreign spectator attendance is
influenced by the buying power in nations, and hence, multiple
scenarios that differ in the composition of qualifying teams must
be considered. For each scenario, the total number of spectators
will depend on the capacity of the stadiums in which matches are
held. Hence the specific match schedule must be taken into
consideration. In Section 1.1, we familiarize the reader with the
FIFA process of group formation and the World Cup schedule
characteristics. In Section 1.2, we review the relevant literature
on sports and tourismmanagement. In Section 1.3, we provide an
outline of the paper.
1.1. Background
Since 1998, teams from thirty-two nations drawn from six
football confederations1 compete in the FIFA World Cup. The
1Asian Football Confederation (AFC); Confederation Africaine de
Football (CAF); The Confederation of North, Central America and
Caribbean Association Football (CONCACAF); Confederacion Sudamer-
icana de Futbol (CONMEBOL); Oceania Football Confederation (OFC);
Union des Associations Europeennes de Football (UEFA).
*Correspondence: Ahmed Ghoniem, Department of Operations and
Information Management, Isenberg School of Management, University of
We point out that a match between high-performing teams
and/or neighboring nations, i.e., with fan participation indices
of 1, will have a popularity value of 4.
The row-set popularity is the sum of the match popularity
values of the matches scheduled in the row. It is only when a
group letter is assigned to each subset of nations that the match
popularity values can be determined. The assignment of group
letters must simultaneously address the impact on the row-
popularity of six different stadiums because the six matches for
each group are held in six distinct stadiums. Since we seek to
estimate maximal possible attendance for planning purposes,
the assignment should balance popular matches across stadiums.
To ensure balanced attendance across all stadiums the model
that we develop maximizes the minimum row-set popularity.
Further, to ensure high attendance at larger-capacity stadiums,
the model also maximizes the maximum row-set popularity.
Given the complexity of this scheduling task, it would be
difficult to construct a heuristic scheme that achieves these
objectives. The notation, variables, and model follow:
Table 3 Data for illustrative example
Nation Code Confederation FIFA points Spectator index (%)
Qatar QAT AFC 300 100Australia AUS AFC 549 77Iran IRN AFC 692 100Japan JPN AFC 617 29Korea Republic KOR AFC 594 13Algeria DZA CAF 986 50Cameroon CMR CAF 646 16Egypt EGY CAF 582 100Nigeria NGA CAF 701 27Tunisia TUN CAF 881 50Costa Rica CRI CONCACAF 1095 3Honduras HND CONCACAF 433 1Mexico MEX CONCACAF 935 63USA USA CONCACAF 828 100Argentina ARG CONMEBOL 1577 54Brazil BRA CONMEBOL 1348 94Chile CHL CONMEBOL 1057 26Paraguay PRY CONMEBOL 434 4Uruguay URY CONMEBOL 1164 9Belgium BEL UEFA 1471 38Croatia HRV UEFA 963 6Czech Republic CZE UEFA 1045 22England ENG UEFA 1031 100France FRA UEFA 1180 51Germany DEU UEFA 1770 63Italy ITA UEFA 1146 51Netherlands NDL UEFA 1415 55Portugal PRT UEFA 1191 34Russia RUS UEFA 788 51Serbia SRB UEFA 709 3Spain ESP UEFA 1130 51Sweden SWE UEFA 663 8
Ahmed Ghoniem et al—Prescriptive analytics for FIFA World Cup lodging capacity planning 1187
Data Sets and Input Parameters
• R: Index set for row-sets.
• G: Index set for the eight subsets of teams obtained from
the Group Formation Model, with the host nation assigned
to Subset 1.
• L � fA;B; . . .;Hg : Group letters. The host nation should
belong, by FIFA convention, to Group A.
• Mg: Set of matches for subset g, 8g 2 G.• qi: Rank order of team i within its subset based on FIFA
points (except for the host nation which, by convention,
leads its subset).
• pg;ði1;i2Þ: Popularity of match ði1; i2Þ in subset g,
8g 2 G; ði1; i2Þ 2 Mg.
• J r: Set of matches in row-set r 2 R. An element of the set is
a triplet that encodes each match assigned to a row-set; e.g.,
• zg‘ 2 f0; 1g: zg‘ ¼ 1 if and only if subset g is assigned to
group letter ‘, 8g 2 G; ‘ 2 L.• yr � 0: Row-set popularity for r 2 R.
• wmin � 0: Minimum row-set popularity.
• wmax � 0: Maximum row-set popularity.
• vr 2 f0; 1g: Binary variable to effect computation of the
maximum row-set popularity 8r 2 R.
Model
Maximize wmin þ wmax ð2aÞ
subject to
wmin � yr; 8r 2 R ð2bÞ
wmax � yr; 8r 2 R ð2cÞ
wmax � yr þ 16ð1� vrÞ; 8r 2 R ð2dÞX
r2Rvr ¼ 1 ð2eÞ
yr¼X
g2G
X
‘2L
X
ði1;i2Þ2Mg :
ð‘;qi1 ;qi2Þ2J r
pg;ði1;i2Þzg‘; 8r2Rð2f Þ
X
g2Gzg‘ ¼ 1; ‘ 2 L ð2gÞ
X
‘2Lzg‘ ¼ 1; g 2 G ð2hÞ
z1A ¼ 1 ð2iÞ
z; v binary; y;wmin;wmax � 0: ð2jÞ
The objective function in (2a) maximizes the sum of the
maximum and minimum row-set popularity values which are
computed via Constraints (2b)–(2e). Constraint (2b) enforces
maximization of the minimum row-set popularity. To enforce
the maximization of the maximum row-set popularity is more
complex and requires the introduction of the auxiliary variable
vr in Constraints (2d) and (2e) without which the objective
would be unbounded. Constraint (2f) computes the row-set
popularity, and Constraints (2g)–(2h) are assignment con-
straints for the eight team subsets and group letters. Constraint
(2i) pre-assigns the host nation subset to group letter A.
Constraint (2j) enforces logical binary and non-negativity
restrictions on variables.
Table 4 Group formation: optimally versus heuristically balanced subsets
Subset Teams Subset points
Optimal1 QAT DEU HRV NGA 37342 ARG ENG IRN HND 37333 BRA CRI SRB EGY 37344 URY ITA RUS CMR 37445 NDL DZA MEX PRY 37706 PRT CZE TUN JPN 37347 FRA ESP USA KOR 37328 BEL CHL SWE AUS 3740
Heuristic1 QAT URY HRV SWE 30902 FRA ITA TUN JPN 38243 NDL CHL SRB CMR 38274 ARG ENG NGA AUS 38585 PRT ESP MEX EGY 38386 BRA CRI RUS KOR 38257 BEL CZE USA PAR 37788 DEU DZA IRN HND 3881
1188 Journal of the Operational Research Society Vol. 68, No. 10
Table
5Detailedgroupstageschedule
forillustrativeexam
ple
Row
Stadium
Capacity
DAY
Row-set
popularity
12
34
56
78
910
11
12
13
14
15
1QAT
ARG
DZA
BRA
DEU
IRN
MEX
EGY
Lusail
86250
3.45
3.32
2.27
3.91
12.94
2HRV
TUN
CHL
ARG
NGA
JPN
AUS
HND
AlRayyan
44740
0.68
1.59
2.08
1.11
5.45
3NDL
PRT
URY
ESP
DZA
CZE
RUS
USA
AlKhor
45330
2.11
1.11
1.19
3.26
7.67
4MEX
SRB
ESP
BEL
PRY
EGY
KOR
AUS
AlSham
al45120
1.36
2.55
1.27
2.31
7.49
5BEL
BRA
FRA
ENG
CHL
CRI
USA
IRN
Khalifa
68030
1.29
1.94
3.26
4.00
10.49
6SWE
ENG
DEU
PRT
AUS
HND
HRV
JPN
AlW
akrah
45120
1.70
2.52
1.39
1.26
6.88
7ARG
QAT
PRT
CHL
Education
ENG
HRV
TUN
SWE
City
45350
3.32
2.60
1.68
0.68
8.27
8IRN
DEU
CZE
URY
HND
NGA
JPN
CMR
AlGharafa
44740
2.52
1.82
1.02
0.49
5.85
9URY
BEL
QAT
CZE
ITA
SWE
NGA
TUN
UmmSlal
45120
1.19
0.91
2.91
1.43
6.44
10
RUS
DZA
CRI
FRA
CMR
PRY
EGY
KOR
DohaPort
44950
1.34
1.09
2.55
1.27
6.25
11
FRA
NDL
BRA
ITA
Sports
ESP
MEX
SRB
RUS
City
47560
2.03
2.38
1.94
2.03
8.38
12
USA
ITA
NDL
CRI
Qatar
KOR
CMR
PRY
SRB
University
43520
2.69
1.34
1.20
0.13
5.35
Ahmed Ghoniem et al—Prescriptive analytics for FIFA World Cup lodging capacity planning 1189
The obtained solution to themodel is used to assign stadiums to
row-sets by maximizingP
s2SP
r2R js�yr , where S is the set of
stadiums with capacities js; 8s 2 S, and �yr; 8 2 R, the optimal
row-set popularity values. The obtained solution in Table 5
reports the capacity of the assigned stadiums, the corresponding
row-set popularity, and the popularity of each of the 48matches,
ranging from 0.13 to 4.00 in our example. The largest capacity
stadium (Lusail with a capacity of 86,250) is assigned to the
row-set with the maximum popularity and the smallest stadium
(Qatar University with a capacity of 43,520) hosts the row-set
with the minimum popularity. In the optimized solution, Group
1 through is assigned to, respectively, letter A, D, H, E, B, G, F,
andC. Consequently, the popularity of 1 is 12.94/16. In contrast,
row-set 12 has the lowest popularity of 5.35/16.
5. Foreign spectator attendance and lodging
In this section, we detail the calculation of the projected
foreign spectator attendance at a match by applying the
spectator indices and FIFA seat allocation percentages to the
capacity of the stadium at which the match is scheduled. This,
in turn, allows the estimation of daily lodging requirements
over the fifteen days of the group stage.
The FIFA seat allocation for each match is based on the
percentage of stadium seats reserved for officials, denoted by
a, and the percentage of the remaining seats, denoted by b, thatis offered to each of the two competing and other nations.
Following FIFA practice, for a match m � ði1; i2Þ 2 Mg
between two nations in subset g, held at a stadium with
capacity jði1;i2Þ, the number of seats reserved for officials is
a� jði1;i2Þ and for each of the two nations and other nations is
ð1� aÞ � b� jði1;i2Þ. The remaining, namely ð1� aÞ�ð1� 3bÞ � jði1;i2Þ, seats are offered to the host nation.
Applying the spectator indices to the allocated seats for a
match, the projected attendance is calculated as follows:
• Number of officials: ~Fm ¼ ~fg;ði1;i2Þ ��ajði1;i2Þ
�.
• Number of spectators from nation 1: Fi1m ¼ fi1��
ð1� aÞbjði1;i2Þ�;
• Number of spectators from nation 2: Fi2m ¼ fi2��
ð1� aÞbjði1;i2Þ�;
• Number of spectators from other nations: Fm ¼ fg;ði1;i2Þ��ð1� aÞbjði1;i2Þ
�.
The lodging requirement for the group stage depends on the
number of nights spent by spectators from qualifying nations,
other nations, and officials. Spectators from a qualifying
nation might attend only one ({1}, {2}, or {3}), two ({1, 2} or
{2, 3}), or three ({1, 2, 3}) group stage matches.3 We assume
that spectators from non-competing nations and officials
attend, on average, a single match. The possibility of extended
stay will be different for spectators from neighboring nations,
non-neighboring nations with low, and non-neighboring
nations with high GDP per capita. We denote the percentages
of spectators from a neighboring nation i that will attend all
three matches by pN123, two matches by pN12 or pN23. Similarly,
percentages for non-neighboring high and low GDP per capita
nations are denoted with a superscript of, respectively, H and
L. These percentages are applied to the number of spectators,
Fim1;Fi
m2;Fi
m3, for nation i attending the individual group stage
matches, m1, m2, and m3, to calculate number of spectators that
attend one or more matches. The computations are as follows:
1. Spectators attending all three matches: Ui;123 ¼pN123 minfFi
m1;Fi
m2;Fi
m3g
j k;
2. Spectators attending matches 2 and 3: Ui;23 ¼pN23 minfFi
m2;Fi
m3g
j k;
Figure 1 Allocated and filled seats for foreign and local spectators for group stage matches.
3The unlikely case where spectators attend matches 1 and 3, but skip
match 2, is ruled out.
1190 Journal of the Operational Research Society Vol. 68, No. 10
3. Spectators attending matches 1 and 2: Ui;12 ¼pN12 minfFi
m1;Fi
m2g
j k;
4. Spectators attending match 1: Ui;1 ¼ Fim1
� Ui;123 � Ui;12;
5. Spectators attending match 2: Ui;2 ¼ Fim2
� Ui;123�Ui;12 � Ui;23;
6. Spectators attending match 3: Ui;3 ¼ Fim3
� Ui;123 � Ui;23.
The number of nights required for each of the six spectator
categories is the number of spectators multiplied by the
number of nights spanning the first and last matches they
attend.
For our example, we use the established FIFA values for the
two seat allocation parameters, a ¼ :09 and b ¼ :12. Figure 1
displays the data reported in Table 6 for the stadium capacity,
Table 6 Attendance at the 48 matches for the illustrative example
Match Day Group Match Match Stadium Foreign Local Foreign Percent attendance
Number Popularity Capacity Allocation Fill Fill Foreign (%) Total (%)
Nine instances of spectator attendance are generated for
each of the 16 scenarios of qualifying nations. The three levels
of probabilities of extended stay that we employ are summa-
rized in Table 7. The three levels of spectator indices are:
(i) Base level which varies from 3% for Honduras to 100% for
England, based on historical data from South Africa 2010 and
Brazil 2014; (ii) an increase of 10% in spectator indices; and
(iii) an increase of 20% in spectator indices.
6.2. Analysis of results
In this section, we first analyze the data obtained from the 144
instances to examine how foreign attendance is impacted by
the specific combination of qualifying nations, spectator index,
and probabilities of extended stay. To do so, we focus on the
effect these three determinants have on the peak lodging
requirement, i.e., the maximum number of hotel rooms,
assuming double occupancy, across the fifteen days of the
group stage.
Figure 3 summarizes the peak lodging requirement for the
16 instances with base parameter settings for spectator index
and probabilities of extended stay. The base peak lodging
requirement averages 67000 rooms and ranges from 63000 to
72000 for the sixteen scenarios. The difference in lodging
requirement can be due to a slight change in the makeup of the
qualifying teams. The scenarios with the least and most
requirement, Scenarios 8 and 9, differ in three qualifying
nations. The spectator indices for the three in Scenario 8 (Cote
d’Ivoire, 0.19; Chile, 0.26; and China, 0.51) are lower than
Figure 3 Base level peak lodging requirement for sixteen scenarios.
Figure 4 Peak attendance and lodging requirement across sixteen scenarios.
Ahmed Ghoniem et al—Prescriptive analytics for FIFA World Cup lodging capacity planning 1193
those for the three with which they are swapped in Scenario 9
(Columbia, 0.38; Algeria, 0.50; and Iraq, 1.00). The difference
has a significant impact on the group composition and stadium
assignment of matches which can result in several popular
matches being held on consecutive days.
As depicted in Figure 4, our analysis reveals that an
incremental increase of 10% in the spectator index values
increases the lodging requirement by about 3000 rooms and an
increase of 5% in the probabilities of extended stay increases
the capacity requirement by 600 rooms. Thus the average
lodging requirement can increase from its base estimated value
of 67000–75000 rooms. This suggests that it is likely that
demand for lodging will require on average 7000–15000 more
rooms than the FIFA minimum requirement of 60000. An
interesting artifact of an increase in the probabilities of
extended stay is that more matches are attended by the same
spectators, with an accompanying reduction in the total
number of foreign spectators in the group stage.
For the host nation’s allocation from FIFA of 1.4 million
seats during the group stage to be filled locally, it would require
460000 football enthusiasts, 20% of the 2.3 million inhabitants
(of which only 280000 are Qatari), to attend at least three
matches. This being improbable, we consider the possibility
that some of these seats would be bought by visiting spectators.
Whereas this would relieve the ‘‘pressure’’ on local attendance,
it would generate a higher demand for lodging. To quantify this
trade-off, we examine an additional 16� 9 instances in which
the host nation’s share of stadium capacity is reduced from 58%
to 47% by increasing the FIFA seat allocation percentage, b,from 12% to 16%. This reallocation of about 11% of the local
seats increases the lodging requirement by a substantial 25%
with the average peak lodging requirement of 85000 rooms.
7. Conclusion
The host nation for a FIFA World Cup must plan lodging
capacity well before the event. The analytics framework for
lodging capacity planning presented in this paper evaluates
alternative scenarios of 32 qualifying teams, incorporating
key determinants of foreign spectator attendance and the
FIFA seat allocation mechanism. Our study reveals that small
differences in the makeup of the qualifying teams can
significantly impact foreign spectator attendance and lodging
demand due to resulting differences in group composition
and the assignment of matches to stadiums. The optimization
methodology is used to examine possible variations in these
factors to determine the range of lodging demand. This, in
turn, informs planning of adequate infrastructure develop-
ment years in advance.
Acknowledgements—This research has been supported by Qatar NationalResearch Fund under Grant Number NPRP 6-248-5-023. We thank QatarTourism Authority for providing data on existing and planned lodging inQatar.
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1194 Journal of the Operational Research Society Vol. 68, No. 10